Align MicroSeries.nullify_weights to the Series index

changelog.d/nullify-weights-index.yaml

@@ -0,0 +1,5 @@
+- bump: patch
+  changes:
+    fixed:
+    - MicroSeries.nullify_weights now aligns weights to the Series index, so
+      aggregations no longer return 0 on a non-default index.

microdf/microseries.py

@@ -157,7 +157,11 @@ def nullify_weights(self) -> None:
         This is useful for comparing weighted and unweighted statistics or when
         you want to temporarily ignore weights.
         """
-        self.weights = pd.Series(np.ones(len(self)), dtype=float)
+        # Index the ones against self.index: weighted ops are label-aligned
+        # (self.multiply(self.weights) in .sum()/.weight()), so a default
+        # RangeIndex here silently produces all-NaN and collapses every
+        # aggregation to 0 whenever the caller uses a non-default index.
+        self.weights = pd.Series(np.ones(len(self)), index=self.index, dtype=float)
 
     @vector_function
     def weight(self) -> pd.Series:

microdf/tests/test_microseries_dataframe.py

@@ -815,3 +815,12 @@ def test_rank_ties_share_bucket() -> None:
     # existing ``test_rank`` expectations hold.
     s = mdf.MicroSeries([1, 2, 3], weights=[4, 5, 6])
     np.testing.assert_array_equal(s.rank().values, [4, 9, 15])
+
+
+def test_nullify_weights_non_default_index():
+    """nullify_weights must align to the index, not a fresh RangeIndex."""
+    s = mdf.MicroSeries([1, 2, 3], index=[10, 11, 12], weights=[1, 2, 3])
+    s.nullify_weights()
+    assert s.sum() == 6
+    assert s.mean() == 2
+    assert list(s.weights.index) == [10, 11, 12]